基于NSP和SVM的滚动轴承故障诊断方法

Fault Diagnosis of Rolling Bearings Based on Null-Space Pursuit Algorithm and Support Vector Machine

针对滚动轴承故障诊断中难以获得大量典型的故障样本,以及统计参数对于滚动轴承故障的多分类效果不理想问题,提出了一种基于零空间追踪算法和支持向量机的故障模式识别方法。首先,根据轴承振动模型估计出相应的微分算子;然后,利用上述微分算子将轴承故障信号分解为一系列具备轴承故障特征的窄带信号之和;最后,计算各窄带信号的统计参数,构造特征向量并利用SVM进行故障模式识别。与传统SVM的对比分析试验结果表明:该方法的诊断准确率高达95%,比传统SVM提高了15%,可有效实现滚动轴承故障的模式识别。

Aiming at the difficulty to obtain enough samples and statistical parameters in roller bearing fault diagnosis, especially the statistical parameters being not ideal for multi-classification of rolling bearing faults,a new method of fault diagnosis and pattern recognition for rolling bearing is proposed based on the algorithm of null-space pursuit and SVM （support vector machine）.First,the null-space differential operator based on the vibration signal model for the faulty bearing fault is established.Then,by using the null-space differential operator proposed,vibration signals to be analyzed are decomposed to a series of narrowband signals.Finally,the statistical parameters of the narrow band signal are calculated to construct the feature vector and the SVM is used to identify the fault patterns.Compared with tradi-tional SVM,the results show that the diagnostic accuracy of this method is as high as 95%,which is 15% higher than that of traditional SVM,which can effectively realize the pattern recognition of rolling bearing fault.